'''simple docstring'''




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        a__ (  UpperCAmelCase__				,  unittest.TestCase					):
     lowerCamelCase    :	Dict				=XLMTokenizer
     lowerCamelCase    :	Optional[Any]				=False






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __lowerCamelCase            =    [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __lowerCamelCase            =    dict(zip(a    ,							range(len(a       )       )       )       )
         __lowerCamelCase            =    ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __lowerCamelCase            =    os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['''vocab_file''']       )
         __lowerCamelCase            =    os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['''merges_file''']       )
         with open(self.vocab_file    ,							'''w'''       ) as fp:
             fp.write(json.dumps(a       )       )
         with open(self.merges_file    ,							'''w'''       ) as fp:
             fp.write('''\n'''.join(a       )       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    '''lower newer'''
         __lowerCamelCase            =    '''lower newer'''
         return input_text, output_text






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    XLMTokenizer(self.vocab_file    ,							self.merges_file       )

         __lowerCamelCase            =    '''lower'''
         __lowerCamelCase            =    ['''low''', '''er</w>''']
         __lowerCamelCase            =    tokenizer.tokenize(a       )
         self.assertListEqual(a    ,							a       )

         __lowerCamelCase            =    tokens + ['''<unk>''']
         __lowerCamelCase            =    [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a       )    ,							a       )






     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''       )

         __lowerCamelCase            =    tokenizer.encode('''sequence builders'''    ,							add_special_tokens=a       )
         __lowerCamelCase            =    tokenizer.encode('''multi-sequence build'''    ,							add_special_tokens=a       )

         __lowerCamelCase            =    tokenizer.build_inputs_with_special_tokens(a       )
         __lowerCamelCase            =    tokenizer.build_inputs_with_special_tokens(a    ,							a       )

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase					={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
